深度学习使结构光照明显微镜能够在低光水平和增强的速度下工作。

Deep learning enables structured illumination microscopy with low light levels and enhanced speed.

机构信息

Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.

Department of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, 310027, Hangzhou, China.

出版信息

Nat Commun. 2020 Apr 22;11(1):1934. doi: 10.1038/s41467-020-15784-x.

DOI:10.1038/s41467-020-15784-x

PMID:32321916

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7176720/

Abstract

Structured illumination microscopy (SIM) surpasses the optical diffraction limit and offers a two-fold enhancement in resolution over diffraction limited microscopy. However, it requires both intense illumination and multiple acquisitions to produce a single high-resolution image. Using deep learning to augment SIM, we obtain a five-fold reduction in the number of raw images required for super-resolution SIM, and generate images under extreme low light conditions (at least 100× fewer photons). We validate the performance of deep neural networks on different cellular structures and achieve multi-color, live-cell super-resolution imaging with greatly reduced photobleaching.

摘要

结构光照明显微镜（SIM）超越了光学衍射极限，在分辨率上比衍射极限显微镜提高了两倍。然而，它需要高强度的照明和多次采集才能生成一张高分辨率图像。我们利用深度学习来增强 SIM，将获得超分辨率 SIM 所需的原始图像数量减少五倍，并在极低光条件下生成图像（至少少 100 倍的光子）。我们在不同的细胞结构上验证了深度神经网络的性能，并实现了多色、活细胞超分辨率成像，同时大大减少了光漂白。